Grid-Optimized Neural Operators (GONOs) for FE-Node-Consistent Thermo-Mechanical Field Reconstruction in Electron-Beam Powder-Bed Fusion.

High-fidelity finite-element (FE) simulations of electron-beam powder-bed fusion (EB-PBF) provide thermo-mechanical fields but are too expensive for repeated field queries. This work proposes grid-optimized neural operators (GONOs), which couple FE nodal data with structured-grid neural operators through a bidirectional point-cloud–grid transformation. The interface projects irregular FE-node fields onto an auxiliary Cartesian grid, applies the neural operator in grid space, and maps the predictions back to the original FE nodes, where training and evaluation are performed. {This retains FE-node-level supervision and evaluation while enabling the use of established structured-grid neural-operator architectures.} The framework is evaluated on a fixed-process, 20-layer Ti-6Al-4V EB-PBF web-spider benchmark. {This study addresses one-shot synchronized-state field reconstruction, defined here as ordinal frame-index-aligned cross-layer reconstruction: the Layer~1 temperature field at intra-step frame s is mapped to temperature, von Mises stress and displacement-magnitude fields for Layers~2--20 at index s of their respective sequential FE analysis steps. Because these steps occur at different global build times, the outputs are neither a simultaneous whole-build snapshot nor an autoregressive time forecast.} Across three independent training seeds at the native 41×41 grid, the improved FNO, enhanced UNet, and MWT variants achieve mean overall relative L₂ errors of 0.01849, 0.01659, and 0.01270, respectively. Tests on irregular quadrilateral and triangular meshes demonstrate that the point-cloud–grid interface remains effective on the tested two-dimensional non-Cartesian FE-node layouts. {A resolution study indicates that an auxiliary-grid multiplier of $r=2.5$ provides a favorable accuracy--cost trade-off for this benchmark.} {At the native grid, with cached P2G inputs, GONOs reconstruct all 19 layer-wise states for the complete 300-sample test set in approximately 7--19~s on a single GPU, including G2P back-projection.} For comparison, generation of the high-fidelity FE reference simulation required approximately 11 days on 120 CPU cores. These results demonstrate an efficient and FE-node-consistent surrogate framework for rapid thermo-mechanical field reconstruction in EB-PBF.